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轻量级深度学习框架用于准确检测与运动有关的骨折

Akmalbek Abdusalomov¹, Sanjar Mirzakhalilov², Sabina Umirzakova¹

¹Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea.

Diagnostics (Basel, Switzerland)

|February 13, 2025

概括

一个新的深度学习框架使用修改的DenseNet121准确检测运动相关的骨折,提高诊断效率和运动员的结果.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
运动医学运动医学

背景情况:

与运动相关的骨折在体育医学中提出了诊断挑战.
对于微妙的骨折,传统的方法可能容易出错,并且效率低下.
准确的骨折检测对于预防并发症和指导治疗至关重要.

研究的目的:

开发一个轻量级和高效的深度学习框架,以改进骨折检测.
为了提高运动相关骨折诊断的准确性和计算效率.
创建一个适合体育医学特定需求的工具.

主要方法:

提出了一个基于DenseNet121架构的新框架,具有修改后的层.
整合了一个Canny边缘探测器,以改善对结构不连续性的检测.
一个定制的体育骨折放射图数据集经过了预处理和增强.

主要成果:

该模型实现了90.3%的准确性,超过了ResNet-50,VGG-16和EfficientNet-B0.
它表现出高灵敏度 (0.89回忆) 和特异性 (0.875精度).
该框架表现出最低的计算复杂度 (0.54G FLOP,14.78M Params).

结论:

这种轻量级的框架为骨折检测提供了可扩展,准确和高效的解决方案.
它解决了体育医学诊断方面的关键挑战.
通过实时部署,改善临床工作流程和运动员结果的潜力.

关键词:

检测骨折检测骨折检测骨折检测骨折轻量级的深度学习放射学分析放射学分析实时诊断的实时诊断.运动医学运动医学

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